{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "842ae0a9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "数据集形状: (168, 148)\n",
      "=== 数据基本信息 ===\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 168 entries, 0 to 167\n",
      "Columns: 148 entries, Class to GLCM3_140\n",
      "dtypes: float64(133), int64(14), object(1)\n",
      "memory usage: 194.4+ KB\n",
      "None\n",
      "\n",
      "=== 前5行数据 ===\n",
      "       Class  BrdIndx  Area  Round  Bright  Compact  ShpIndx  Mean_G  Mean_R  \\\n",
      "0       car      1.27    91   0.97  231.38     1.39     1.47  207.92  241.74   \n",
      "1  concrete      2.36   241   1.56  216.15     2.46     2.51  187.85  229.39   \n",
      "2  concrete      2.12   266   1.47  232.18     2.07     2.21  206.54  244.22   \n",
      "3  concrete      2.42   399   1.28  230.40     2.49     2.73  204.60  243.27   \n",
      "4  concrete      2.15   944   1.73  193.18     2.28     4.10  165.98  205.55   \n",
      "\n",
      "   Mean_NIR  ...  SD_NIR_140  LW_140  GLCM1_140  Rect_140  GLCM2_140  \\\n",
      "0    244.48  ...       26.18    2.00       0.50      0.85       6.29   \n",
      "1    231.20  ...       22.29    2.25       0.79      0.55       8.42   \n",
      "2    245.79  ...       15.59    2.19       0.76      0.74       7.24   \n",
      "3    243.32  ...       13.51    3.34       0.82      0.74       7.44   \n",
      "4    208.00  ...       15.65   50.08       0.85      0.49       8.15   \n",
      "\n",
      "   Dens_140  Assym_140  NDVI_140  BordLngth_140  GLCM3_140  \n",
      "0      1.67       0.70     -0.08             56    3806.36  \n",
      "1      1.38       0.81     -0.09           1746    1450.14  \n",
      "2      1.68       0.81     -0.07            566    1094.04  \n",
      "3      1.36       0.92     -0.09           1178    1125.38  \n",
      "4      0.23       1.00     -0.08           6232    1146.38  \n",
      "\n",
      "[5 rows x 148 columns]\n",
      "\n",
      "=== 数值特征统计 ===\n",
      "          BrdIndx         Area       Round      Bright     Compact  \\\n",
      "count  168.000000   168.000000  168.000000  168.000000  168.000000   \n",
      "mean     2.008512   565.869048    1.132976  165.569821    2.077679   \n",
      "std      0.634807   679.852886    0.489150   61.883993    0.699600   \n",
      "min      1.000000    10.000000    0.020000   37.670000    1.000000   \n",
      "25%      1.537500   178.000000    0.787500  133.977500    1.547500   \n",
      "50%      1.920000   315.000000    1.085000  164.485000    1.940000   \n",
      "75%      2.375000   667.000000    1.410000  221.895000    2.460000   \n",
      "max      4.190000  3659.000000    2.890000  244.740000    4.700000   \n",
      "\n",
      "          ShpIndx      Mean_G      Mean_R    Mean_NIR        SD_G  ...  \\\n",
      "count  168.000000  168.000000  168.000000  168.000000  168.000000  ...   \n",
      "mean     2.229881  161.577083  163.672440  171.459226   10.131369  ...   \n",
      "std      0.703572   63.407201   71.306748   67.973969    5.179409  ...   \n",
      "min      1.060000   30.680000   32.210000   40.120000    4.330000  ...   \n",
      "25%      1.700000   91.040000  101.187500  120.165000    6.770000  ...   \n",
      "50%      2.130000  187.560000  160.615000  178.345000    8.010000  ...   \n",
      "75%      2.680000  210.940000  234.815000  236.002500   11.500000  ...   \n",
      "max      4.300000  246.350000  253.080000  253.320000   36.400000  ...   \n",
      "\n",
      "       SD_NIR_140      LW_140   GLCM1_140    Rect_140   GLCM2_140    Dens_140  \\\n",
      "count  168.000000  168.000000  168.000000  168.000000  168.000000  168.000000   \n",
      "mean    23.769881    3.098274    0.796488    0.665000    7.795536    1.594405   \n",
      "std     12.836522    6.101883    0.103930    0.179086    0.670491    0.460627   \n",
      "min      4.020000    1.000000    0.330000    0.240000    6.290000    0.230000   \n",
      "25%     13.965000    1.395000    0.757500    0.560000    7.357500    1.325000   \n",
      "50%     21.135000    1.740000    0.810000    0.690000    7.790000    1.660000   \n",
      "75%     29.957500    2.285000    0.870000    0.810000    8.260000    1.945000   \n",
      "max     60.020000   51.540000    0.950000    0.980000    9.340000    2.340000   \n",
      "\n",
      "        Assym_140    NDVI_140  BordLngth_140    GLCM3_140  \n",
      "count  168.000000  168.000000     168.000000   168.000000  \n",
      "mean     0.615357    0.014583     983.309524  1275.292917  \n",
      "std      0.239900    0.153677     880.013745   603.658611  \n",
      "min      0.070000   -0.360000      56.000000   336.730000  \n",
      "25%      0.460000   -0.080000     320.000000   817.405000  \n",
      "50%      0.620000   -0.040000     776.000000  1187.025000  \n",
      "75%      0.810000    0.120000    1412.500000  1588.427500  \n",
      "max      1.000000    0.350000    6232.000000  3806.360000  \n",
      "\n",
      "[8 rows x 147 columns]\n",
      "\n",
      "=== 缺失值检查 ===\n",
      "Class            0\n",
      "BrdIndx          0\n",
      "Area             0\n",
      "Round            0\n",
      "Bright           0\n",
      "                ..\n",
      "Dens_140         0\n",
      "Assym_140        0\n",
      "NDVI_140         0\n",
      "BordLngth_140    0\n",
      "GLCM3_140        0\n",
      "Length: 148, dtype: int64\n",
      "训练集形状: (117, 147), 测试集形状: (51, 147)\n",
      "\n",
      "=== 类别分布分析 ===\n",
      "类别分布: Counter({'grass ': 20, 'building ': 17, 'concrete ': 16, 'tree ': 12, 'shadow ': 11, 'car ': 11, 'soil ': 10, 'asphalt ': 10, 'pool ': 10})\n",
      "类别权重: {'asphalt ': 1.3, 'building ': 0.7647058823529411, 'car ': 1.1818181818181819, 'concrete ': 0.8125, 'grass ': 0.65, 'pool ': 1.3, 'shadow ': 1.1818181818181819, 'soil ': 1.3, 'tree ': 1.0833333333333333}\n",
      "\n",
      "=== 十折交叉验证结果 ===\n",
      "RandomForest 平均准确率: 0.8303 (±0.1227)\n",
      "SVM 平均准确率: 0.7538 (±0.0999)\n",
      "LogisticRegression 平均准确率: 0.7008 (±0.1205)\n",
      "\n",
      "=== RandomForest 分类报告 ===\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.67      1.00      0.80         4\n",
      "           1       0.86      0.75      0.80         8\n",
      "           2       0.80      1.00      0.89         4\n",
      "           3       0.80      0.57      0.67         7\n",
      "           4       0.88      0.78      0.82         9\n",
      "           5       1.00      0.80      0.89         5\n",
      "           6       1.00      0.60      0.75         5\n",
      "           7       0.67      1.00      0.80         4\n",
      "           8       0.71      1.00      0.83         5\n",
      "\n",
      "    accuracy                           0.80        51\n",
      "   macro avg       0.82      0.83      0.81        51\n",
      "weighted avg       0.83      0.80      0.80        51\n",
      "\n",
      "\n",
      "=== SVM 分类报告 ===\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00         4\n",
      "           1       0.88      0.88      0.88         8\n",
      "           2       0.67      1.00      0.80         4\n",
      "           3       1.00      0.71      0.83         7\n",
      "           4       0.88      0.78      0.82         9\n",
      "           5       1.00      0.80      0.89         5\n",
      "           6       1.00      0.80      0.89         5\n",
      "           7       0.80      1.00      0.89         4\n",
      "           8       0.71      1.00      0.83         5\n",
      "\n",
      "    accuracy                           0.86        51\n",
      "   macro avg       0.88      0.89      0.87        51\n",
      "weighted avg       0.89      0.86      0.86        51\n",
      "\n",
      "\n",
      "=== LogisticRegression 分类报告 ===\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.80      1.00      0.89         4\n",
      "           1       0.80      0.50      0.62         8\n",
      "           2       1.00      1.00      1.00         4\n",
      "           3       0.67      0.86      0.75         7\n",
      "           4       0.55      0.67      0.60         9\n",
      "           5       1.00      0.80      0.89         5\n",
      "           6       1.00      0.80      0.89         5\n",
      "           7       1.00      0.75      0.86         4\n",
      "           8       0.50      0.60      0.55         5\n",
      "\n",
      "    accuracy                           0.75        51\n",
      "   macro avg       0.81      0.77      0.78        51\n",
      "weighted avg       0.78      0.75      0.75        51\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 39044 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 27979 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 26631 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 31614 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 30495 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 23454 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 28151 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 28102 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 30697 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 38453 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 28909 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 21147 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 22270 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 720x576 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 39044 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 27979 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 26631 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 31614 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 30495 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 23454 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 28151 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 28102 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 30697 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 38453 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 28909 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 21147 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 22270 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 720x576 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 720x576 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 20551 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 27491 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 29575 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 22810 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 20998 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 31867 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 26354 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 32447 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 30495 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 23439 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 24179 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 22343 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 20551 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 27491 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 29575 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 30495 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 22810 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 20998 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 31867 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 26354 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 32447 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 23439 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 24179 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 22343 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 20551 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 27491 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 29575 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 22810 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 20998 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 31867 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 26354 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 32447 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 30495 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 23439 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 24179 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 22343 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 20551 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 27491 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 29575 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 30495 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 22810 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 20998 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 31867 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 26354 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 32447 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 23439 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 24179 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 22343 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 20551 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 27491 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 29575 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 22810 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 20998 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 31867 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 26354 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 32447 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 30495 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 23439 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 24179 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 22343 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 20551 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 27491 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 29575 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 30495 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 22810 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 20998 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 31867 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 26354 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 32447 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 23439 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 24179 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 22343 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from sklearn.model_selection import train_test_split, StratifiedKFold, cross_val_score\n",
    "from sklearn.preprocessing import StandardScaler, LabelEncoder\n",
    "from sklearn.impute import SimpleImputer\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.metrics import (accuracy_score, precision_score, recall_score, f1_score, \n",
    "                             confusion_matrix, classification_report, roc_curve, auc, \n",
    "                             RocCurveDisplay, ConfusionMatrixDisplay)\n",
    "from sklearn.utils.class_weight import compute_class_weight\n",
    "from sklearn.multiclass import OneVsRestClassifier\n",
    "from collections import Counter\n",
    "\n",
    "# 设置中文字体和图形样式\n",
    "plt.rcParams['font.sans-serif'] = ['SimHei']\n",
    "plt.rcParams['axes.unicode_minus'] = False\n",
    "sns.set_style(\"whitegrid\")\n",
    "\n",
    "# 1. 加载数据\n",
    "def load_data(file_path):\n",
    "    \"\"\"加载数据集\"\"\"\n",
    "    data = pd.read_csv(file_path)\n",
    "    print(\"数据集形状:\", data.shape)\n",
    "    return data\n",
    "\n",
    "# 2. 数据探索和预处理\n",
    "def explore_data(data):\n",
    "    \"\"\"数据探索\"\"\"\n",
    "    print(\"=== 数据基本信息 ===\")\n",
    "    print(data.info())\n",
    "    \n",
    "    print(\"\\n=== 前5行数据 ===\")\n",
    "    print(data.head())\n",
    "    \n",
    "    print(\"\\n=== 数值特征统计 ===\")\n",
    "    print(data.describe())\n",
    "    \n",
    "    print(\"\\n=== 缺失值检查 ===\")\n",
    "    print(data.isnull().sum())\n",
    "    \n",
    "    return data\n",
    "\n",
    "# 3. 处理类别不平衡\n",
    "def handle_class_imbalance(y):\n",
    "    \"\"\"处理类别不平衡问题\"\"\"\n",
    "    print(\"\\n=== 类别分布分析 ===\")\n",
    "    class_distribution = Counter(y)\n",
    "    print(\"类别分布:\", class_distribution)\n",
    "    \n",
    "    # 计算类别权重\n",
    "    class_weights = compute_class_weight('balanced', classes=np.unique(y), y=y)\n",
    "    print(\"类别权重:\", dict(zip(np.unique(y), class_weights)))\n",
    "    \n",
    "    return class_weights\n",
    "\n",
    "# 4. 手动分割数据集\n",
    "def split_dataset(data, target_column='Class', test_size=0.3, random_state=42):\n",
    "    \"\"\"手动分割训练集和测试集\"\"\"\n",
    "    X = data.drop(columns=[target_column])\n",
    "    y = data[target_column]\n",
    "    \n",
    "    # 分割数据集\n",
    "    X_train, X_test, y_train, y_test = train_test_split(\n",
    "        X, y, test_size=test_size, random_state=random_state, stratify=y\n",
    "    )\n",
    "    \n",
    "    print(f\"训练集形状: {X_train.shape}, 测试集形状: {X_test.shape}\")\n",
    "    return X_train, X_test, y_train, y_test\n",
    "\n",
    "# 5. 数据预处理\n",
    "def preprocess_data(X_train, X_test):\n",
    "    \"\"\"数据预处理\"\"\"\n",
    "    # 处理缺失值\n",
    "    imputer = SimpleImputer(strategy='mean')\n",
    "    X_train_imputed = imputer.fit_transform(X_train)\n",
    "    X_test_imputed = imputer.transform(X_test)\n",
    "    \n",
    "    # 标准化特征\n",
    "    scaler = StandardScaler()\n",
    "    X_train_scaled = scaler.fit_transform(X_train_imputed)\n",
    "    X_test_scaled = scaler.transform(X_test_imputed)\n",
    "    \n",
    "    return X_train_scaled, X_test_scaled, scaler\n",
    "\n",
    "# 6. 训练多种分类模型\n",
    "def train_models(X_train, y_train):\n",
    "    \"\"\"训练多种分类模型\"\"\"\n",
    "    models = {\n",
    "        'RandomForest': RandomForestClassifier(random_state=42, class_weight='balanced'),\n",
    "        'SVM': SVC(random_state=42, probability=True, class_weight='balanced'),\n",
    "        'LogisticRegression': LogisticRegression(random_state=42, class_weight='balanced', max_iter=1000)\n",
    "    }\n",
    "    \n",
    "    # 十折交叉验证\n",
    "    print(\"\\n=== 十折交叉验证结果 ===\")\n",
    "    for name, model in models.items():\n",
    "        cv_scores = cross_val_score(model, X_train, y_train, cv=10, scoring='accuracy')\n",
    "        print(f\"{name} 平均准确率: {cv_scores.mean():.4f} (±{cv_scores.std():.4f})\")\n",
    "    \n",
    "    # 训练最终模型\n",
    "    trained_models = {}\n",
    "    for name, model in models.items():\n",
    "        model.fit(X_train, y_train)\n",
    "        trained_models[name] = model\n",
    "    \n",
    "    return trained_models\n",
    "\n",
    "# 7. 评估模型性能\n",
    "def evaluate_models(models, X_test, y_test):\n",
    "    \"\"\"评估模型性能\"\"\"\n",
    "    results = {}\n",
    "    \n",
    "    for name, model in models.items():\n",
    "        y_pred = model.predict(X_test)\n",
    "        y_proba = model.predict_proba(X_test) if hasattr(model, 'predict_proba') else None\n",
    "        \n",
    "        results[name] = {\n",
    "            'accuracy': accuracy_score(y_test, y_pred),\n",
    "            'precision': precision_score(y_test, y_pred, average='weighted'),\n",
    "            'recall': recall_score(y_test, y_pred, average='weighted'),\n",
    "            'f1': f1_score(y_test, y_pred, average='weighted'),\n",
    "            'confusion_matrix': confusion_matrix(y_test, y_pred),\n",
    "            'y_proba': y_proba\n",
    "        }\n",
    "        \n",
    "        print(f\"\\n=== {name} 分类报告 ===\")\n",
    "        print(classification_report(y_test, y_pred))\n",
    "    \n",
    "    return results\n",
    "\n",
    "# 8. 绘制混淆矩阵热力图\n",
    "def plot_confusion_matrix_heatmap(cm, model_name, classes):\n",
    "    \"\"\"绘制混淆矩阵热力图\"\"\"\n",
    "    plt.figure(figsize=(10, 8))\n",
    "    disp = ConfusionMatrixDisplay(confusion_matrix=cm, display_labels=classes)\n",
    "    disp.plot(cmap='Blues', values_format='d')\n",
    "    plt.title(f'{model_name} - 混淆矩阵热力图')\n",
    "    plt.ylabel('真实标签')\n",
    "    plt.xlabel('预测标签')\n",
    "    plt.xticks(rotation=45)\n",
    "    plt.tight_layout()\n",
    "    plt.show()\n",
    "\n",
    "# 9. 绘制ROC曲线和计算AUC（支持多分类）\n",
    "def plot_roc_curve_multiclass(y_test, y_proba, model_name, classes):\n",
    "    \"\"\"绘制多分类ROC曲线\"\"\"\n",
    "    n_classes = len(classes)\n",
    "    \n",
    "    # 计算每个类别的ROC曲线和AUC\n",
    "    fpr = dict()\n",
    "    tpr = dict()\n",
    "    roc_auc = dict()\n",
    "    \n",
    "    for i in range(n_classes):\n",
    "        fpr[i], tpr[i], _ = roc_curve(y_test == i, y_proba[:, i])\n",
    "        roc_auc[i] = auc(fpr[i], tpr[i])\n",
    "    \n",
    "    # 计算宏平均ROC曲线和AUC\n",
    "    all_fpr = np.unique(np.concatenate([fpr[i] for i in range(n_classes)]))\n",
    "    mean_tpr = np.zeros_like(all_fpr)\n",
    "    for i in range(n_classes):\n",
    "        mean_tpr += np.interp(all_fpr, fpr[i], tpr[i])\n",
    "    mean_tpr /= n_classes\n",
    "    fpr[\"macro\"] = all_fpr\n",
    "    tpr[\"macro\"] = mean_tpr\n",
    "    roc_auc[\"macro\"] = auc(fpr[\"macro\"], tpr[\"macro\"])\n",
    "    \n",
    "    # 绘制所有ROC曲线\n",
    "    plt.figure(figsize=(10, 8))\n",
    "    plt.plot(fpr[\"macro\"], tpr[\"macro\"],\n",
    "             label=f'宏平均 ROC曲线 (AUC = {roc_auc[\"macro\"]:.2f})',\n",
    "             color='navy', linestyle=':', linewidth=4)\n",
    "    \n",
    "    colors = plt.cm.rainbow(np.linspace(0, 1, n_classes))\n",
    "    for i, color in zip(range(n_classes), colors):\n",
    "        plt.plot(fpr[i], tpr[i], color=color, lw=2,\n",
    "                 label=f'{classes[i]} (AUC = {roc_auc[i]:.2f})')\n",
    "    \n",
    "    plt.plot([0, 1], [0, 1], 'k--', lw=2)\n",
    "    plt.xlim([0.0, 1.0])\n",
    "    plt.ylim([0.0, 1.05])\n",
    "    plt.xlabel('假正率')\n",
    "    plt.ylabel('真正率')\n",
    "    plt.title(f'{model_name} - 多分类ROC曲线')\n",
    "    plt.legend(loc=\"lower right\")\n",
    "    plt.show()\n",
    "    \n",
    "    return roc_auc\n",
    "\n",
    "# 主函数\n",
    "def main():\n",
    "    # 加载数据\n",
    "    data = load_data('land_train.csv')\n",
    "    \n",
    "    # 数据探索\n",
    "    data = explore_data(data)\n",
    "    \n",
    "    # 分割数据集\n",
    "    X_train, X_test, y_train, y_test = split_dataset(data)\n",
    "    \n",
    "    # 处理类别不平衡\n",
    "    class_weights = handle_class_imbalance(y_train)\n",
    "    \n",
    "    # 数据预处理\n",
    "    X_train_scaled, X_test_scaled, scaler = preprocess_data(X_train, X_test)\n",
    "    \n",
    "    # 编码目标变量\n",
    "    label_encoder = LabelEncoder()\n",
    "    y_train_encoded = label_encoder.fit_transform(y_train)\n",
    "    y_test_encoded = label_encoder.transform(y_test)\n",
    "    classes = label_encoder.classes_\n",
    "    \n",
    "    # 训练模型\n",
    "    models = train_models(X_train_scaled, y_train_encoded)\n",
    "    \n",
    "    # 评估模型\n",
    "    results = evaluate_models(models, X_test_scaled, y_test_encoded)\n",
    "    \n",
    "    # 绘制混淆矩阵热力图\n",
    "    for name, result in results.items():\n",
    "        plot_confusion_matrix_heatmap(result['confusion_matrix'], name, classes)\n",
    "    \n",
    "    # 绘制ROC曲线（支持多分类）\n",
    "    for name, result in results.items():\n",
    "        if result['y_proba'] is not None:\n",
    "            plot_roc_curve_multiclass(y_test_encoded, result['y_proba'], name, classes)\n",
    "\n",
    "if __name__ == \"__main__\":\n",
    "    main()\n",
    "\n",
    "\n",
    "# In[ ]:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f703f05c",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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